Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling
Read the original on arXiv AI →The paper introduces Quality-Controllable Policy (QCP), a robot policy that allows real‑time adjustment of quality attributes such as speed and smoothness during task execution. QCP employs an iterative hindsight relabeling loop, where online rollouts are collected under target quality labels, successful trajectories are added to the dataset, and trajectories are relabeled based on measured quality before fine‑tuning the policy. Experiments on Robomimic, RoboEval, and real‑world manipulation tasks show that QCP improves both task execution quality and success rates, enabling higher throughput in conveyor operations and better stability when carrying fragile objects.
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